SAM: Significance analysis of microarrays - simple user interface

Description

Correlates a large number of features (eg genes) with an outcome
variable, such as a group indicator, quantitative variable or survival time.
This is a simple user interface for the samr function applied to array data.
For sequencing data applications, see the function SAMseq.

Optional initial seed for random number generator (integer)

logged2

Has the data been transformed by log (base 2)? This information is used only
for computing fold changes

fdr.output

eigengene.number

Eigengene to be used (just for resp.type="Pattern discovery")

Details

This is a simple, user-friendly interface to the samr package used on array data.
It calls samr, samr.compute.delta.table and samr.compute.siggenes.table.
samr detects differential expression for microarray data,
and sequencing data,
and other data with a large number of features. samr is the R package
that is called by the "official" SAM Excel Addin.
The format of the response vector y and the calling sequence
is illustrated in the examples below. A more complete description
is given in the SAM manual
at http://www-stat.stanford.edu/~tibs/SAM

Value

A list with components

samr.obj

Output of samr. See documentation for samr for details.

siggenes.table

Table of significant genes, output of samr.compute.siggenes.table.
This has components: genes.up— matrix of significant genes having positive correlation with the outcome and
genes.lo—matrix of significant genes having negative correlation with the outcome.
For survival data, genes.up are those genes having positive correlation with risk-
that is, increased expression corresponds to higher risk (shorter survival)
genes.lo are those whose increased expression corresponds to lower risk (longer survival).

delta.table

Output of samr.compute.delta.table.

del

Value of delta (distance from 45 degree line in SAM plot) for
used for creating delta.table and siggenes.table. Changing the input value fdr.output
will change the resulting del.